Recent trends in socio-epidemic modelling: behaviours and their determinants

TL;DR

This paper reviews socio-epidemic models, focusing on how behavioural factors influence disease spread, and highlights the complexity of individual responses beyond simple awareness or trust assumptions, based on COVID-19 data analysis.

Contribution

It provides a comprehensive overview of modelling approaches and challenges the common linear assumptions by analyzing empirical social indicator data during COVID-19.

Findings

Behavioural responses are poorly explained by awareness, beliefs, or trust.

Most models assume linear correlation between behaviours and determinants.

Empirical data suggests the need for more complex behavioural models.

Abstract

The spreading dynamics of infectious diseases is influenced by individual behaviours, which are in turn affected by the level of awareness about the epidemic. Modelling the co-evolution of disease transmission and behavioural changes within a population enables better understanding, prediction and control of epidemics. Here, our primary goal is to provide an overview of the most popular modelling approaches, ranging from compartmental mean-field to agent-based models, with a particular focus on how behavioural factors are incorporated into epidemic dynamics. We classify modelling approaches based on the fundamental conceptual distinction between models of behaviours and models of behavioural determinants (such as awareness, beliefs, opinions, or trust); in particular, we observe that most studies model and interpret the variables related to individual responses either as behaviours or as determinants, with the implicit assumption that they correlate linearly. Based on preliminary empirical observations, we then challenge this assumption by analysing a recent dataset about time series of social indicators, collected during the COVID-19 pandemic. We examine the case study of Italian regions and we discover that behavioural responses are poorly explained by awareness, beliefs or trust, thereby calling for a careful interpretation of the modelling assumptions and for the development of further models, which fully account for the inherent complexity of individual responses and human behaviours.